ABSTRACT Autism Spectrum Disorder (ASD) is a complex brain disorder marked by difficulties in verbal and non- verbal social interactions and patterns of repetitive behaviors. Of the approximately 4 million babies born in the U.S. each year, about 60,000 will be diagnosed with ASD, or about 1 in 59 children based on the recent CDC estimate. Interventions delivered as early as 12 months of age have been shown to be effective, and early diagnosis is critical to the success of ASD interventions. However, there has been little progress on identifying children at high risk for ASD at an early age. Although universal screening could improve the early detection of ASD, various barriers have kept it from being widely adopted. Decades of in-depth research have not only identified behavioral ASD markers but have also shed light on many other risk factors, such as genetic variants, family and siblings? medical history, brain abnormalities, low birth weight, and paternal and maternal ages at childbirth. In addition, there is inconclusive evidence that some medical conditions, such as otitis media, infections, epilepsy, gastrointestinal problems, birth complications, and delay in developmental and physiological milestones, may be associated with ASD, but may manifest well before the onset of hallmark behavioral symptoms of ASD. Despite the fact that individually these medical symptoms may not be sensitive enough to be used as a viable marker for ASD diagnosis, combined together, they hold the promise of robustly determining children?s risks for ASD well before any existing ASD screening tool is currently capable of. The elimination of delayed diagnosis would allow for optimization of outcomes through early intervention. To the best of our knowledge, there has been little research to harvest this accumulated knowledge. By leveraging a large, national, longitudinal, private insurance medical claims database (MarketScan) and Medicaid claims database (Medicaid Analytic eXtract or MAX), we will comprehensively investigate the collective role of certain medical symptoms and healthcare service use patterns as early markers for predicting ASD risk. If successful, this study will demonstrate a novel way of improving ASD risk prediction, upon which we can construct a medical claims-based ASD surveillance system. Working in the background, such a system can sift through an extensive volume of children?s electronic medical claims records, looking for patterns indicating potential risk and identifying children for further in-person evaluations when their ASD risk has crossed a critical threshold. This would ultimately advance ASD early detection and thus ultimately improve the impact of early intervention therapies.